Epidemiologic Reviews最新文献

Improving race and ethnicity (hereafter, race/ethnicity) data quality is imperative to ensure underserved populations are represented in data sets used to identify health disparities and inform health care policy. We performed a scoping review of methods that retrospectively improve race/ethnicity classification in secondary data sets. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, searches were conducted in the MEDLINE, Embase, and Web of Science Core Collection databases in July 2022. A total of 2 441 abstracts were dually screened, 453 full-text articles were reviewed, and 120 articles were included. Study characteristics were extracted and described in a narrative analysis. Six main method types for improving race/ethnicity data were identified: expert review (n = 9; 8%), name lists (n = 27, 23%), name algorithms (n = 55, 46%), machine learning (n = 14, 12%), data linkage (n = 9, 8%), and other (n = 6, 5%). The main racial/ethnic groups targeted for classification were Asian (n = 56, 47%) and White (n = 51, 43%). Some form of validation evaluation was included in 86 articles (72%). We discuss the strengths and limitations of different method types and potential harms of identified methods. Innovative methods are needed to better identify racial/ethnic subgroups and further validation studies. Accurately collecting and reporting disaggregated data by race/ethnicity are critical to address the systematic missingness of relevant demographic data that can erroneously guide policymaking and hinder the effectiveness of health care practices and intervention.

Asians are likely to experience a high burden of chronic conditions, including, but not limited to, diabetes, cardiovascular disease, and cancer, due to differences in biologic, genetic, and environmental factors across Asian ethnic groups. A diagnosis of any chronic condition can contribute to increased mental health burdens, including depression, psychological distress, and posttraumatic stress disorder (PTSD). However, few studies have examined these comorbid conditions across distinct Asian ethnic groups-an important limitation given the differences in social, cultural, and behavioral drivers of mental health burdens within and across Asian ethnicities. To understand the disparities in mental health burdens among Asians living with a chronic health condition, we conducted a systematic literature review of relevant, peer-reviewed publication databases to identify studies reporting on mental health burdens (e.g., depression, anxiety, distress, PTSD) in distinct Asian ethnic groups in North America. Thirteen studies met the inclusion criteria for this review and collectively demonstrated a high burden of depression, psychological distress, and PTSD among Asians living with chronic conditions. Moreover, there were distinct disparities in mental health burdens across chronic conditions and across Asian ethnic groups. Despite the detrimental impact of poor mental health on chronic disease-specific outcomes, such as death and poor quality of life, few data exist that characterize mental health outcomes among Asian ethnicities living in North America with chronic conditions. Future work should prioritize estimating the national prevalence of mental health outcomes among adults with chronic conditions, by Asian ethnicities, to inform culturally tailored interventions to address this public health burden.

Although the literature on the differences between Black people and White people in terms of differences in major depressive disorder and related self-reported symptoms is robust, less robust is the literature on how these outcomes are patterned within the US Black population and why differences exist. Given increased ethnic diversity of Black Americans due to increases in immigration, continued aggregation may mask differences between Black ethnic-immigrant groups and Black Americans with more distant ancestral ties to Africa (African Americans). The purpose of this narrative review was to comprehensively synthesize the literature on depression and related symptoms within the US Black population across immigration- and ethnicity-related domains and provide a summary of mechanisms proposed to explain variation. Findings revealed substantial variation in the presence of these outcomes within the US Black population by nativity, region of birth, age at immigration, and Caribbean ethnic origin. Racial context and racial socialization were identified as important, promising mechanisms for better understanding variations by region of birth and among those born or socialized in the United States, respectively. Findings warrant data collection efforts and measurement innovation to better account for within-racial differences in outcomes under study. A greater appreciation of the growing ethnic-immigrant diversity within the US Black population may improve understanding of how racism differentially functions as a cause of depression and related symptoms within this group.

Indigenous people are often misracialized as other racial or ethnic identities in population health research. This misclassification leads to underestimation of Indigenous-specific mortality and health metrics, and subsequently, inadequate resource allocation. In recognition of this problem, investigators around the world have devised analytic methods to address racial misclassification of Indigenous people. We carried out a scoping review based on searches in PubMed, Web of Science, and the Native Health Database for empirical studies published after 2000 that include Indigenous-specific estimates of health or mortality and that take analytic steps to rectify racial misclassification of Indigenous people. We then considered the weaknesses and strengths of implemented analytic approaches, with a focus on methods used in the US context. To do this, we extracted information from 97 articles and compared the analytic approaches used. The most common approach to address Indigenous misclassification is to use data linkage; other methods include geographic restriction to areas where misclassification is less common, exclusion of some subgroups, imputation, aggregation, and electronic health record abstraction. We identified 4 primary limitations of these approaches: (1) combining data sources that use inconsistent processes and/or sources of race and ethnicity information; (2) conflating race, ethnicity, and nationality; (3) applying insufficient algorithms to bridge, impute, or link race and ethnicity information; and (4) assuming the hyperlocality of Indigenous people. Although there is no perfect solution to the issue of Indigenous misclassification in population-based studies, a review of this literature provided information on promising practices to consider.

Comparisons between randomized trial analyses and observational analyses that attempt to address similar research questions have generated many controversies in epidemiology and the social sciences. There has been little consensus on when such comparisons are reasonable, what their implications are for the validity of observational analyses, or whether trial and observational analyses can be integrated to address effectiveness questions. Here, we consider methods for using observational analyses to complement trial analyses when assessing treatment effectiveness. First, we review the framework for designing observational analyses that emulate target trials and present an evidence map of its recent applications. We then review approaches for estimating the average treatment effect in the target population underlying the emulation: using observational analyses of the emulation data alone; and using transportability analyses to extend inferences from a trial to the target population. We explain how comparing treatment effect estimates from the emulation against those from the trial can provide evidence on whether observational analyses can be trusted to deliver valid estimates of effectiveness - a process we refer to as benchmarking - and, in some cases, allow the joint analysis of the trial and observational data. We illustrate different approaches using a simplified example of a pragmatic trial and its emulation in registry data. We conclude that synthesizing trial and observational data - in transportability, benchmarking, or joint analyses - can leverage their complementary strengths to enhance learning about comparative effectiveness, through a process combining quantitative methods and epidemiological judgements.

Although observational studies have identified modifiable risk factors for Alzheimer disease and related dementias (ADRD), randomized controlled trials (RCTs) of risk factor modification for ADRD prevention have been inconsistent or inconclusive. This finding suggests a need to improve translation between observational studies and RCTs. However, many common features of observational studies reduce their relevance to designing related RCTs. Observational studies routinely differ from RCTs with respect to eligibility criteria, study population, length of follow-up, treatment conditions, outcomes, and effect estimates. Using the motivating example of blood pressure reduction for ADRD prevention, we illustrate the need for a tighter connection between observational studies and RCTs, discuss barriers to using typically reported observational evidence in developing RCTs, and highlight methods that may be used to make observational research more relevant to clinical trial design. We conclude that the questions asked and answered by observational research can be made more relevant to clinical trial design and that better use of observational data may increase the likelihood of successful, or at least definitive, trials. Although we focus on improving translation of observational studies on risk factors for ADRD to RCTs in ADRD prevention, the overarching themes are broadly applicable to many areas of biomedical research.

Randomized trials are often designed to collect outcomes at fixed points in time after randomization. In practice, the number and timing of outcome assessments can vary among participants (i.e., irregular assessment). In fact, the timing of assessments may be associated with the outcome of interest (i.e., informative assessment). For example, in a trial evaluating the effectiveness of treatments for major depressive disorder, not only did the timings of outcome assessments vary among participants but symptom scores were associated with assessment frequency. This type of informative observation requires appropriate statistical analysis. Although analytic methods have been developed, they are rarely used. In this article, we review the literature on irregular assessments with a view toward developing recommendations for analyzing trials with irregular and potentially informative assessment times. We show how the choice of analytic approach hinges on assumptions about the relationship between the assessment and outcome processes. We argue that irregular assessment should be treated with the same care as missing data, and we propose that trialists adopt strategies to minimize the extent of irregularity; describe the extent of irregularity in assessment times; make their assumptions about the relationships between assessment times and outcomes explicit; adopt analytic techniques that are appropriate to their assumptions; and assess the sensitivity of trial results to their assumptions.

Increasing attention has been paid to the risks and benefits of terminating large clinical trials before reaching prespecified targets, because such decisions can greatly affect the implementation of findings. The Department of Veterans Affairs (VA) Cooperative Studies Program (CSP) is a research infrastructure dedicated to conducting high-quality clinical research. A scoping review was performed to characterize barriers preventing the attainment of prespecified recruitment, statistical power, or sample-size targets in VA CSP trials. A trial was eligible for inclusion if the trial was sponsored by the VA CSP, primary findings were published within the last 10 years, and a decision was made to terminate enrollment or follow-up before meeting a priori recruitment or endpoint targets. In 11 of 29 included trials (37.9%), a decision was made to terminate the trial early. The most common reason for early termination was related to under-recruitment (n = 5). Other reasons included early detection of safety signals (n = 2), futility (n = 1), and benefit (n = 1). This review highlights recruitment as a critical facet of trial conduct that may hinder the production of high-quality data and thus warrant additional attention. Solutions to enhance recruitment now implemented by the VA CSP, including dedicated enrollment infrastructure and screening facilitated by informatics approaches, show promise in reducing this cause for early termination.

In clinical trials, harms (i.e., adverse events) are often reported by simply counting the number of people who experienced each event. Reporting only frequencies ignores other dimensions of the data that are important for stakeholders, including severity, seriousness, rate (recurrence), timing, and groups of related harms. Additionally, application of selection criteria to harms prevents most from being reported. Visualization of data could improve communication of multidimensional data. We replicated and compared the characteristics of 6 different approaches for visualizing harms: dot plot, stacked bar chart, volcano plot, heat map, treemap, and tendril plot. We considered binary events using individual participant data from a randomized trial of gabapentin for neuropathic pain. We assessed their value using a heuristic approach and a group of content experts. We produced all figures using R and share the open-source code on GitHub. Most original visualizations propose presenting individual harms (e.g., dizziness, somnolence) alone or alongside higher level (e.g., by body systems) summaries of harms, although they could be applied at either level. Visualizations can present different dimensions of all harms observed in trials. Except for the tendril plot, all other plots do not require individual participant data. The dot plot and volcano plot are favored as visualization approaches to present an overall summary of harms data. Our value assessment found the dot plot and volcano plot were favored by content experts. Using visualizations to report harms could improve communication. Trialists can use our provided code to easily implement these approaches.

Clinical trials are considered the gold standard for establishing efficacy of health interventions, thus determining which interventions are brought to scale in health care and public health programs. Digital clinical trials, broadly defined as trials that have partial to full integration of technology across implementation, interventions, and/or data collection, are valued for increased efficiencies as well as testing of digitally delivered interventions. Although recent reviews have described the advantages and disadvantages of and provided recommendations for improving scientific rigor in the conduct of digital clinical trials, few to none have investigated how digital clinical trials address the digital divide, whether they are equitably accessible, and if trial outcomes are potentially beneficial only to those with optimal and consistent access to technology. Human immunodeficiency virus (HIV), among other health conditions, disproportionately affects socially and economically marginalized populations, raising questions of whether interventions found to be efficacious in digital clinical trials and subsequently brought to scale will sufficiently and consistently reach and provide benefit to these populations. We reviewed examples from HIV research from across geographic settings to describe how digital clinical trials can either reproduce or mitigate health inequities via the design and implementation of the digital clinical trials and, ultimately, the programs that result. We discuss how digital clinical trials can be intentionally designed to prevent inequities, monitor ongoing access and utilization, and assess for differential impacts among subgroups with diverse technology access and use. These findings can be generalized to many other health fields and are practical considerations for donors, investigators, reviewers, and ethics committees engaged in digital clinical trials.